sanmit at cs dot utexas dot edu
Office: GDC 3.424E
I'm a PhD student in the Department of Computer Science at the University of Texas at Austin, advised by Peter Stone. My research is on curriculum learning -- the automated design of a sequence of tasks that enable autonomous agents to learn faster or better. I'm also a member of the UT Austin Villa Standard Platform League team, where I work primarily on the vision system.
I received my BS in computer science from California State University, Los Angeles, where I worked on using probabilistic automata (e.g. HMMs, etc.) for anomaly detection in computer agents with Professor Valentino Crespi. I also worked part time at the Jet Propulsion Laboratory on using multispectral and synthetic aperture radar data to detect natural disasters such as flooding, etc., and on telemetry processing.
Fall 2015: TA for CS 344M: Autonomous Multiagent Systems
Fall 2016: TA for CS 394R: Reinforcement Learning: Theory and Practice
Sanmit Narvekar, Jivko Sinapov, and Peter Stone. Autonomous Task Sequencing for Customized Curriculum Design in Reinforcement Learning.
In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, Australia, August 19-25, 2017. [pdf] [bib]
Sanmit Narvekar, Jivko Sinapov, Matteo Leonetti, and Peter Stone. Source Task Creation for Curriculum Learning.
In Proceedings of the 15th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2016), Singapore, May 9-13, 2016. [pdf] [bib] [slides]
Jivko Sinapov, Sanmit Narvekar, Matteo Leonetti, and Peter Stone. Learning Inter-Task Transferability in the Absence of Target Task Samples.
In Proceedings of the 14th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2015), Istanbul, Turkey, May 4-8, 2015. [pdf] [bib]